Compute a confidence interval from sample data
I have sample data which I would like to compute a confidence interval for, assuming a normal distribution.
I have sample data which I would like to compute a confidence interval for, assuming a normal distribution.
Why do we use ‘loc’ for pandas dataframes? it seems the following code with or without using loc both compile anr run at a simulular speed
How can I add a custom filter to django admin (the filters that appear on the right side of a model dashboard)? I know its easy to include a filter based on a field of that model, but what about a “calculated” field like this:
I’m trying to pull nested values from a json file. I want to print out each of the values for every “id” key. I think I’m close but can’t figure out why the obj type changes from a dict to a list, and then why I’m unable to parse that list.
Here is a link to the json I’m working with: http://hastebin.com/ratevimixa.tex
From an example you can see a multiple OR query filter:
I can write something myself by finding zero-crossings of the first derivative or something, but it seems like a common-enough function to be included in standard libraries. Anyone know of one?
In the math module, I could only find math.cos(x), with cos/sin/tan/acos/asin/atan. This returns the answer in radians. How can I get the answer in degrees?
At work there’s a script that lists completed tasks. This was written by someone else and is hosted over the network. I have an alias in my .bashrc that calls this script, with its many flags and such, and I wanted to write a python script that would call this alias every few minutes so I can have a shell open with updated stats. However, subprocess.call("myAlias") fails. I’m still fairly new to python, and am struggling to figure this out.
First question is what is the difference between Value and Manager().Value?
I have a dataframe in pandas called ‘munged_data’ with two columns ‘entry_date’ and ‘dob’ which i have converted to Timestamps using pd.to_timestamp.I am trying to figure out how to calculate ages of people based on the time difference between ‘entry_date’ and ‘dob’ and to do this i need to get the difference in days between the two columns ( so that i can then do somehting like round(days/365.25). I do not seem to be able to find a way to do this using a vectorized operation. When I do munged_data.entry_date-munged_data.dob i get the following :